Building digital twins of articulated objects and scenes through interactive perception

Perceiving and interacting with complex household environments with human-level robustness and flexibility is an open challenge in embodied AI and robotics. Despite significant progress in AI-powered robotics, manipulation algorithms currently lack the necessary generalization and robustness required for widespread deployment. We aim to close this gap by building digital twins of the real world, which can be instantiated in physical simulation for developing and validating mobile manipulation algorithms. Our work proposed to build the digital twins of objects and indoor scenes from the robot's embodied experience in the real world. Experiments show that our approach can be applied to the real scenario and reconstruct an accurate articulation model without any modification.